Clinical evaluation of patients in an Intensive Care Unit (ICU) often rely on diagnostic images, such as portable chest radiographic images, for example. It has been noted that chest radiographs can be particularly helpful in the ICU for indicating significant or unexpected conditions requiring changes in patient management. For this reason, a number of such images may be obtained periodically from the patient during a treatment period in the ICU.
To meet the need for readily accessible and rapid diagnostic imaging, equipment such as portable chest radiography equipment has been developed, allowing the ICU clinician to conveniently obtain a radiographic image as needed for the patient. A single diagnostic image may show a condition related to treatment procedures, such as a collapsed lung, for example, or the proper or improper placement of tubing within the patient. A succession of diagnostic images, taken over a time period, may help to show the progress of a patient's condition and help direct ICU treatment accordingly. While portable radiography has advantages for improving patient care, however, there are some difficulties that limit the accuracy and usefulness of diagnostic images in the ICU. Differences in image quality from one image to the next can be significant, owing to differences in exposure settings, patient and apparatus positioning, scattering, and grid application, for example. Thus, even for images obtained from the same patient over a short treatment interval, there can be substantial differences between two or more images that prevent effective comparison between them and constrain the ability of the clinician to detect subtle changes that can be highly significant. This problem relates to images whether originally obtained on film and scanned, or digitally obtained, such as using a computed radiography (CR) or digital radiography (DR) system.
Computed radiography systems that use storage phosphors and digital radiography systems can offer a very wide exposure latitude (as much as 10,000:1) compared with that available from conventional screen/film systems, (typically 40:1). This means that exposure error is much less serious for computed radiography at the time of image sensing and recording. However, image display apparatus have a much more limited dynamic range. Tone scale mapping in computed radiography can be specifically tailored to provide an optimal rendition of every individual image. However, most output media, such as photographic film and cathode ray tube (CRT) displays, do not have wide enough dynamic ranges to display the 10,000:1 latitude of information with proper visual contrast. It is, therefore, necessary to carefully allocate the available output dynamic range to display the clinically important part of the input code values. For some applications, the range of the region of interest in the input image may exceed that provided by the output media or display, and the contrast of parts of the input image may then be compromised as a result. For example, U.S. Pat. No. 4,302,672 entitled “Image Gradation Processing Method And Apparatus For Radiation Image Recording System” to Kato et al. teaches a method of constructing such a compromised tone-scale curve for chest x-ray images. However, that method uses the valleys and peaks of the code-value histogram to identify the critical points between the spine, the heart, and the lung. The results are not very reliable because these valleys and peaks are not always clearly detectable. This method requires that all images obtained have the same overall spatial profile, which need not be true. Furthermore, the method cannot be generalized to examinations other than chest images.
There are generally five classes of “objects” in radiographic images: (1) foreground (collimator blades used to protect parts of the body from unnecessary x-ray exposure) usually corresponding to very low to low exposure areas; (2) man-made objects (such as pacemakers, tubes, and electrodes); (3) soft tissues (such as muscles, blood vessels, and intestines) usually correspond to low (e.g., mediastinum) to high (e.g., lung) exposures depending on the thickness; (4) bones corresponding to the very low to low exposures (often overlapping with the foreground); and (5) background corresponding to very high exposure areas. These five classes of objects can be difficult to separate using the code value alone because, there can be considerable overlaps (such as with the bone and the collimator blades).
As noted in commonly assigned U.S. Pat. No. 5,633,511 entitled “Automatic Tone Scale Adjustment Using Image Activity Measures” to Lee et al., two issues in adjusting tone scale for computed radiography relate, to: (1) determining which sub-range of the input code values is most important for clinical evaluation and (2) constructing a tone-scale transfer curve so that the important sub-range of the code values identified in step (1) can be rendered with proper contrast and brightness (density) on the output media. For example, the digital code values of an input chest x-ray image may span from 500 to 3000 (in units of 0.001 log exposure), but, the code value range of the lung area, being the most important region of the image, may span from about 1800 to 2600. Simply mapping the entire range of the input code value (from 500 to 3000) to the available film density range with equal contrast for all input code values produces a chest image with an unacceptably low contrast. It is, therefore, desirable to have an automatic algorithm to detect and select the relevant sub-range of the input code values (typically 1800 to 2600) to display on the output media with proper visual contrast and brightness. The process of selecting the relevant sub-range of input code values and constructing the proper mapping function from the input code value to the output display media is termed tone scale adjustment.
The Lee et al. '511 disclosure describes conventional approaches for identifying the sub-range of interest in the image, using a histogram of input code values, then discloses an alternative for identifying this sub-range, using an activity histogram. The activity histogram disclosed in the Lee et al. '511 patent gives a measure of line-by-line image activity that improves overall image rendering and has advantages for achieving improved image contrast and brightness.
Relating to the techniques of the Lee et al. '511 patent, a contrast enhancement method is disclosed in commonly assigned U.S. Pat. No. 6,778,691 entitled “Method of Automatically Determining Tone-Scale Parameters for a Digital Image” to Barski et al. The method of the Barski et all. '691 disclosure automatically generates a Look-Up Table (LUT) for obtaining a desired tone scale for an image, using the slope of the tone scale curve over its mid-range densities.
Conventional methods for adjusting the intensity range and slope of diagnostic image values have not provided completely satisfactory results. While methods such as those described in the Lee et al. '511 patent and in the Barski et al. '691 patent provide improvements in contrast enhancement for a diagnostic image, these methods do not address the problem of consistent rendering between images taken for a patient at different times. For example, where two or more images for a patient taken at different times differ with respect to exposure values or other values, application of such contrast improvement techniques is not likely to provide consistent rendering that would allow more accurate assessment of condition changes by the ICU clinician.
Contrast stretching is one method that has been proposed for providing a measure of normalization between images. For example, U.S. Pat. No. 5,357,549 (Maack) describes a dynamic range compression technique that stretches image intensity in only a particular area of interest, such as within the lung area of a chest X-ray. The proposed method locates low frequency components, determines equalization factors, and then applies these to the image for compressing low frequency components, freeing the remainder of the dynamic range for higher frequency areas of the image intensities. In a similar approach, U.S. Pat. No. 5,835,618 (Fang) uses a method of dynamic range remapping for enhancing the image in both dark and bright intensity areas. This remapping or correction technique amounts to smoothing the data (such as through a low-pass filter), determining the data mean, adjusting the smoothed data to the mean, and then applying smoothed, adjusted data to the original data. However, these and other conventional contrast-stretching methods may result in unacceptable levels of loss or distortion of the original image data.
Thus, there remains a need for consistent rendering of diagnostic images taken over a period of time, particularly for patients in an ICU or similar care facility.